Overview

Dataset statistics

Number of variables10
Number of observations5696
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory445.1 KiB
Average record size in memory80.0 B

Variable types

Numeric10

Alerts

avg_bsize_quantity is highly overall correlated with avg_bsize_variety and 2 other fieldsHigh correlation
avg_bsize_variety is highly overall correlated with avg_bsize_quantity and 1 other fieldsHigh correlation
gross_revenue is highly overall correlated with avg_bsize_quantity and 2 other fieldsHigh correlation
invoice_no is highly overall correlated with gross_revenue and 4 other fieldsHigh correlation
purchase_frequency is highly overall correlated with invoice_noHigh correlation
qtt_prod_purchased is highly overall correlated with avg_bsize_quantity and 3 other fieldsHigh correlation
qtt_returns is highly overall correlated with invoice_noHigh correlation
recency_days is highly overall correlated with invoice_noHigh correlation
gross_revenue is highly skewed (γ1 = 21.63051372)Skewed
avg_ticket is highly skewed (γ1 = 53.25360013)Skewed
qtt_returns is highly skewed (γ1 = 52.03318787)Skewed
avg_bsize_quantity is highly skewed (γ1 = 48.54036937)Skewed
customer_id has unique valuesUnique
qtt_returns has 4191 (73.6%) zerosZeros

Reproduction

Analysis started2023-12-04 20:00:35.363701
Analysis finished2023-12-04 20:00:54.400213
Duration19.04 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

UNIQUE 

Distinct5696
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12499.694
Minimum1901
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-04T16:00:54.566033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1901
5-th percentile2530.75
Q112420.75
median14372
Q316319.5
95-th percentile17893.25
Maximum18287
Range16386
Interquartile range (IQR)3898.75

Descriptive statistics

Standard deviation5234.6342
Coefficient of variation (CV)0.41878099
Kurtosis-0.62231677
Mean12499.694
Median Absolute Deviation (MAD)1950
Skewness-0.9729705
Sum71198257
Variance27401395
MonotonicityNot monotonic
2023-12-04T16:00:54.821151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
12424 1
 
< 0.1%
12922 1
 
< 0.1%
3997 1
 
< 0.1%
16589 1
 
< 0.1%
13730 1
 
< 0.1%
16866 1
 
< 0.1%
3995 1
 
< 0.1%
3994 1
 
< 0.1%
3993 1
 
< 0.1%
Other values (5686) 5686
99.8%
ValueCountFrequency (%)
1901 1
< 0.1%
1910 1
< 0.1%
1911 1
< 0.1%
1912 1
< 0.1%
1914 1
< 0.1%
1915 1
< 0.1%
1916 1
< 0.1%
1917 1
< 0.1%
1918 1
< 0.1%
1921 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18281 1
< 0.1%
18280 1
< 0.1%
18278 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION 

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.90713
Minimum0
Maximum373
Zeros38
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-04T16:00:55.268725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q122.75
median71
Q3200
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)177.25

Descriptive statistics

Standard deviation111.64731
Coefficient of variation (CV)0.95500859
Kurtosis-0.64230967
Mean116.90713
Median Absolute Deviation (MAD)61
Skewness0.81450073
Sum665903
Variance12465.122
MonotonicityNot monotonic
2023-12-04T16:00:55.613260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 110
 
1.9%
4 105
 
1.8%
3 98
 
1.7%
2 92
 
1.6%
10 86
 
1.5%
8 82
 
1.4%
17 79
 
1.4%
9 79
 
1.4%
7 78
 
1.4%
15 66
 
1.2%
Other values (294) 4821
84.6%
ValueCountFrequency (%)
0 38
 
0.7%
1 110
1.9%
2 92
1.6%
3 98
1.7%
4 105
1.8%
5 52
0.9%
7 78
1.4%
8 82
1.4%
9 79
1.4%
10 86
1.5%
ValueCountFrequency (%)
373 23
0.4%
372 23
0.4%
371 17
0.3%
369 4
 
0.1%
368 13
0.2%
367 16
0.3%
366 15
0.3%
365 19
0.3%
364 11
0.2%
362 7
 
0.1%

invoice_no
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4710323
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-04T16:00:55.949821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum206
Range205
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.812724
Coefficient of variation (CV)1.9627371
Kurtosis302.14125
Mean3.4710323
Median Absolute Deviation (MAD)0
Skewness13.193891
Sum19771
Variance46.413208
MonotonicityNot monotonic
2023-12-04T16:00:56.295472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2871
50.4%
2 826
 
14.5%
3 503
 
8.8%
4 394
 
6.9%
5 237
 
4.2%
6 173
 
3.0%
7 138
 
2.4%
8 98
 
1.7%
9 69
 
1.2%
10 55
 
1.0%
Other values (46) 332
 
5.8%
ValueCountFrequency (%)
1 2871
50.4%
2 826
 
14.5%
3 503
 
8.8%
4 394
 
6.9%
5 237
 
4.2%
6 173
 
3.0%
7 138
 
2.4%
8 98
 
1.7%
9 69
 
1.2%
10 55
 
1.0%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
< 0.1%
60 1
< 0.1%
57 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5450
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1803.5693
Minimum0.42
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-04T16:00:56.575379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile13.1925
Q1236.1575
median613.93
Q31571.07
95-th percentile5321.925
Maximum279138.02
Range279137.6
Interquartile range (IQR)1334.9125

Descriptive statistics

Standard deviation7896.7316
Coefficient of variation (CV)4.378391
Kurtosis608.27299
Mean1803.5693
Median Absolute Deviation (MAD)479.79
Skewness21.630514
Sum10273131
Variance62358370
MonotonicityNot monotonic
2023-12-04T16:00:56.867620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.95 9
 
0.2%
2.95 8
 
0.1%
1.25 8
 
0.1%
4.95 8
 
0.1%
12.75 7
 
0.1%
1.65 7
 
0.1%
3.75 7
 
0.1%
4.25 6
 
0.1%
5.95 6
 
0.1%
7.5 6
 
0.1%
Other values (5440) 5624
98.7%
ValueCountFrequency (%)
0.42 1
 
< 0.1%
0.65 1
 
< 0.1%
0.79 1
 
< 0.1%
0.84 4
0.1%
0.85 3
 
0.1%
1.07 1
 
< 0.1%
1.25 8
0.1%
1.44 1
 
< 0.1%
1.65 7
0.1%
1.69 1
 
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
77183.6 1
< 0.1%
72882.09 1
< 0.1%

avg_ticket
Real number (ℝ)

SKEWED 

Distinct5503
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.598333
Minimum0.42
Maximum77183.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-04T16:00:57.241023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile3.4602048
Q17.95
median15.854356
Q321.997509
95-th percentile76.32
Maximum77183.6
Range77183.18
Interquartile range (IQR)14.047509

Descriptive statistics

Standard deviation1281.4464
Coefficient of variation (CV)23.470431
Kurtosis2951.7891
Mean54.598333
Median Absolute Deviation (MAD)7.4972705
Skewness53.2536
Sum310992.11
Variance1642104.9
MonotonicityNot monotonic
2023-12-04T16:00:57.640378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.75 11
 
0.2%
4.95 10
 
0.2%
1.25 9
 
0.2%
2.95 9
 
0.2%
7.95 8
 
0.1%
1.65 7
 
0.1%
8.25 7
 
0.1%
12.75 7
 
0.1%
15 6
 
0.1%
4.15 6
 
0.1%
Other values (5493) 5616
98.6%
ValueCountFrequency (%)
0.42 3
0.1%
0.535 1
 
< 0.1%
0.65 1
 
< 0.1%
0.79 1
 
< 0.1%
0.8371428571 1
 
< 0.1%
0.84 2
< 0.1%
0.85 3
0.1%
1.002222222 1
 
< 0.1%
1.02 1
 
< 0.1%
1.03875 1
 
< 0.1%
ValueCountFrequency (%)
77183.6 1
< 0.1%
56157.5 1
< 0.1%
13305.5 1
< 0.1%
4453.43 1
< 0.1%
3861 1
< 0.1%
3202.92 1
< 0.1%
3096 1
< 0.1%
1687.2 1
< 0.1%
1377.077778 1
< 0.1%
1001.2 1
< 0.1%

qtt_prod_purchased
Real number (ℝ)

HIGH CORRELATION 

Distinct529
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.59375
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-04T16:00:57.981115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q114
median41
Q3106
95-th percentile332.25
Maximum7838
Range7837
Interquartile range (IQR)92

Descriptive statistics

Standard deviation210.56346
Coefficient of variation (CV)2.2740569
Kurtosis510.35701
Mean92.59375
Median Absolute Deviation (MAD)33
Skewness17.754316
Sum527414
Variance44336.969
MonotonicityNot monotonic
2023-12-04T16:00:58.312070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 256
 
4.5%
2 149
 
2.6%
3 109
 
1.9%
10 101
 
1.8%
6 99
 
1.7%
9 92
 
1.6%
5 91
 
1.6%
4 87
 
1.5%
11 83
 
1.5%
7 83
 
1.5%
Other values (519) 4546
79.8%
ValueCountFrequency (%)
1 256
4.5%
2 149
2.6%
3 109
1.9%
4 87
 
1.5%
5 91
 
1.6%
6 99
 
1.7%
7 83
 
1.5%
8 81
 
1.4%
9 92
 
1.6%
10 101
 
1.8%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

purchase_frequency
Real number (ℝ)

HIGH CORRELATION 

Distinct1226
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54720228
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-04T16:00:58.707142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.011029412
Q10.024922118
median1
Q31
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.97507788

Descriptive statistics

Standard deviation0.55025519
Coefficient of variation (CV)1.0055791
Kurtosis139.11774
Mean0.54720228
Median Absolute Deviation (MAD)0
Skewness4.8582008
Sum3116.8642
Variance0.30278077
MonotonicityNot monotonic
2023-12-04T16:00:59.032593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2879
50.5%
2 47
 
0.8%
0.0625 18
 
0.3%
0.02777777778 17
 
0.3%
0.02380952381 16
 
0.3%
0.08333333333 15
 
0.3%
0.09090909091 15
 
0.3%
0.03448275862 14
 
0.2%
0.02941176471 14
 
0.2%
0.02127659574 13
 
0.2%
Other values (1216) 2648
46.5%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
< 0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
< 0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
< 0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
4 1
 
< 0.1%
3 5
 
0.1%
2 47
 
0.8%
1.142857143 1
 
< 0.1%
1 2879
50.5%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%

qtt_returns
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct215
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.482444
Minimum0
Maximum80995
Zeros4191
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-04T16:00:59.329155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile39
Maximum80995
Range80995
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1469.4607
Coefficient of variation (CV)32.308305
Kurtosis2757.0725
Mean45.482444
Median Absolute Deviation (MAD)0
Skewness52.033188
Sum259068
Variance2159314.7
MonotonicityNot monotonic
2023-12-04T16:00:59.627347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4191
73.6%
1 169
 
3.0%
2 150
 
2.6%
3 105
 
1.8%
4 89
 
1.6%
6 78
 
1.4%
5 61
 
1.1%
12 52
 
0.9%
7 44
 
0.8%
8 43
 
0.8%
Other values (205) 714
 
12.5%
ValueCountFrequency (%)
0 4191
73.6%
1 169
 
3.0%
2 150
 
2.6%
3 105
 
1.8%
4 89
 
1.6%
5 61
 
1.1%
6 78
 
1.4%
7 44
 
0.8%
8 43
 
0.8%
9 41
 
0.7%
ValueCountFrequency (%)
80995 1
< 0.1%
74215 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%

avg_bsize_quantity
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2370
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.17771
Minimum1
Maximum74215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-04T16:00:59.882613image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q175
median151.83333
Q3290.63542
95-th percentile734.25
Maximum74215
Range74214
Interquartile range (IQR)215.63542

Descriptive statistics

Standard deviation1199.095
Coefficient of variation (CV)4.4712703
Kurtosis2768.8614
Mean268.17771
Median Absolute Deviation (MAD)96.75
Skewness48.540369
Sum1527540.2
Variance1437828.9
MonotonicityNot monotonic
2023-12-04T16:01:00.186912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 115
 
2.0%
2 72
 
1.3%
3 51
 
0.9%
4 49
 
0.9%
5 35
 
0.6%
6 29
 
0.5%
12 26
 
0.5%
100 22
 
0.4%
72 22
 
0.4%
73 21
 
0.4%
Other values (2360) 5254
92.2%
ValueCountFrequency (%)
1 115
2.0%
2 72
1.3%
3 51
0.9%
3.333333333 1
 
< 0.1%
4 49
0.9%
5 35
 
0.6%
5.333333333 1
 
< 0.1%
5.666666667 1
 
< 0.1%
6 29
 
0.5%
6.142857143 1
 
< 0.1%
ValueCountFrequency (%)
74215 1
< 0.1%
40498.5 1
< 0.1%
14149 1
< 0.1%
13956 1
< 0.1%
7824 1
< 0.1%
6009.333333 1
< 0.1%
5963 1
< 0.1%
5197 1
< 0.1%
4300 1
< 0.1%
4282 1
< 0.1%

avg_bsize_variety
Real number (ℝ)

HIGH CORRELATION 

Distinct1171
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.249488
Minimum0.2
Maximum1109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-04T16:01:00.457740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1
Q17.2401316
median15
Q331
95-th percentile173
Maximum1109
Range1108.8
Interquartile range (IQR)23.759868

Descriptive statistics

Standard deviation76.877573
Coefficient of variation (CV)2.0638558
Kurtosis32.894154
Mean37.249488
Median Absolute Deviation (MAD)10
Skewness5.0739525
Sum212173.09
Variance5910.1612
MonotonicityNot monotonic
2023-12-04T16:01:00.699412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 278
 
4.9%
2 161
 
2.8%
3 115
 
2.0%
10 105
 
1.8%
9 105
 
1.8%
8 103
 
1.8%
5 101
 
1.8%
6 101
 
1.8%
7 101
 
1.8%
13 97
 
1.7%
Other values (1161) 4429
77.8%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 7
0.1%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.2%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
1109 1
< 0.1%
748 1
< 0.1%
730 1
< 0.1%
720 1
< 0.1%
703 1
< 0.1%
686 1
< 0.1%
675 1
< 0.1%
673 1
< 0.1%
660 1
< 0.1%
649 1
< 0.1%

Interactions

2023-12-04T16:00:51.851636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:35.716848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:37.460393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:39.318572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:41.081273image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:42.766770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:44.522146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:46.468312image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:48.343803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:50.241955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:52.003103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:35.890575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:37.660619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:39.510268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:41.233717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:42.943612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:44.679180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:46.613032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:48.535127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:50.427349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:52.164628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:36.054142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:37.835071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:39.681695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:41.405510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:43.091389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:44.831680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:46.776808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:48.746704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:50.567600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:52.314273image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:36.210651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:38.017962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:39.849628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:41.578150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:43.263819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:44.994827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:46.930704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:48.886019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:50.728337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:52.464025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:36.347888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:38.161054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:39.982641image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:41.705159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:43.446496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:45.151797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:47.186535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:49.054694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:50.880941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:52.663345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:36.554642image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:38.328137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:40.144625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:41.930693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:43.586052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:45.341347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:47.408872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:49.287866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:51.050481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:52.892145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:36.742872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:38.497399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:40.370208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:42.090003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:43.777537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:45.721636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:47.559258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:49.508747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:51.215603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:53.070683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:36.905040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:38.635588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:40.539291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:42.254009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:43.967004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:45.892671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:47.745018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:49.705311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:51.361770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:53.491742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:37.121964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:38.803808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:40.725681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:42.428249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:44.154313image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:46.099593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:47.953723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:49.888317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:51.540889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:53.648201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:37.281236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:38.965839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:40.917588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:42.592510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:44.331711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:46.301084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:48.157648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:50.034383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-04T16:00:51.716151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-04T16:01:00.896485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
avg_bsize_quantityavg_bsize_varietyavg_ticketcustomer_idgross_revenueinvoice_nopurchase_frequencyqtt_prod_purchasedqtt_returnsrecency_days
avg_bsize_quantity1.0000.5700.2660.0590.7220.157-0.1290.6110.181-0.199
avg_bsize_variety0.5701.000-0.373-0.1390.370-0.1800.1030.657-0.1020.050
avg_ticket0.266-0.3731.0000.2450.3450.267-0.229-0.1410.269-0.148
customer_id0.059-0.1390.2451.0000.1080.383-0.3530.0520.213-0.325
gross_revenue0.7220.3700.3450.1081.0000.644-0.4530.8350.434-0.426
invoice_no0.157-0.1800.2670.3830.6441.000-0.7990.5330.538-0.597
purchase_frequency-0.1290.103-0.229-0.353-0.453-0.7991.000-0.359-0.3660.486
qtt_prod_purchased0.6110.657-0.1410.0520.8350.533-0.3591.0000.315-0.378
qtt_returns0.181-0.1020.2690.2130.4340.538-0.3660.3151.000-0.320
recency_days-0.1990.050-0.148-0.325-0.426-0.5970.486-0.378-0.3201.000

Missing values

2023-12-04T16:00:53.884810image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-04T16:00:54.241888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idrecency_daysinvoice_nogross_revenueavg_ticketqtt_prod_purchasedpurchase_frequencyqtt_returnsavg_bsize_quantityavg_bsize_variety
017850.0372345391.2118.15222229717.00000040.050.9705880.617647
113047.05693232.5918.9040351710.02830235.0154.44444411.666667
212583.02156705.3828.9025002320.04032350.0335.2000007.600000
313748.0955948.2533.866071280.0179210.087.8000004.800000
415100.03333876.00292.00000030.07317122.026.6666670.333333
515291.025144623.3045.3264711020.04011529.0150.1428574.357143
614688.07215630.8717.2197863270.057221399.0172.4285717.047619
717809.016125411.9188.719836610.03352041.0171.4166673.833333
815311.009160767.9025.54346423790.243316474.0419.7142866.230769
916098.08772005.6329.934776670.0243900.087.5714294.857143
customer_idrecency_daysinvoice_nogross_revenueavg_ticketqtt_prod_purchasedpurchase_frequencyqtt_returnsavg_bsize_quantityavg_bsize_variety
56865600.0114839.4278.055161621.00.01074.055.0
568713298.011360.00180.00000021.00.096.02.0
568814569.011227.3918.949167121.00.079.010.0
56895604.01117.902.55714371.00.014.07.0
56905605.0113.351.67500021.00.02.02.0
56915606.0115699.008.9889596341.00.01747.0634.0
56925607.0016756.069.2548777301.00.02010.0730.0
56935608.0013217.2054.528814591.00.0654.056.0
56945609.0013950.7218.2060832171.00.0731.0217.0
569512713.001794.5521.474324371.00.0505.037.0